Correlation between mRNAseq expression and clinical features
Adrenocortical Carcinoma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1TM793F
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1TM793F
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 17733 genes and 7 clinical features across 79 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

- 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

- LMNB2|84823 , DNA2|1763 , DAZAP1|26528 , UBE2S|27338 , TMEM194A|23306 , ...

- 1 gene correlated to 'YEARS_TO_BIRTH'.

- FAM40B|57464

- 30 genes correlated to 'NEOPLASM_DISEASESTAGE'.

- DIAPH3|81624 , YJEFN3|374887 , C14ORF80|283643 , ZWILCH|55055 , C8ORFK29|340393 , ...

- 30 genes correlated to 'PATHOLOGY_T_STAGE'.

- SETD8|387893 , FANCI|55215 , LMNB2|84823 , MCM10|55388 , DIAPH3|81624 , ...

- 17 genes correlated to 'GENDER'.

- CLN8|2055 , MAFG|4097 , DPYSL2|1808 , FAM117A|81558 , SFRS8|6433 , ...

- No genes correlated to 'PATHOLOGY_N_STAGE', and 'ETHNICITY'.

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature | Statistical test | Significant genes | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
DAYS_TO_DEATH_OR_LAST_FUP | Cox regression test | N=30 | shorter survival | N=27 | longer survival | N=3
YEARS_TO_BIRTH | Spearman correlation test | N=1 | older | N=1 | younger | N=0
NEOPLASM_DISEASESTAGE | Kruskal-Wallis test | N=30 |  |  |  |
PATHOLOGY_T_STAGE | Spearman correlation test | N=30 | higher stage | N=30 | lower stage | N=0
PATHOLOGY_N_STAGE | Wilcoxon test | N=0 |  |  |  |
GENDER | Wilcoxon test | N=17 | male | N=17 | female | N=0
ETHNICITY | Wilcoxon test | N=0 |  |  |  |

Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 genes related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1. Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP | Duration (Months) | 4.1-153.6 (median=36.3)
| censored | N = 52
| death | N = 26
| Significant markers | N = 30
| associated with shorter survival | 27
| associated with longer survival | 3

List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2. Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
LMNB2|84823 | 6.5 | 4.645e-10 | 4.5e-06 | 0.872
DNA2|1763 | 3.1 | 5.026e-10 | 4.5e-06 | 0.848
DAZAP1|26528 | 25 | 2.14e-09 | 1.3e-05 | 0.787
UBE2S|27338 | 3.8 | 3.377e-09 | 1.3e-05 | 0.855
TMEM194A|23306 | 4.5 | 3.738e-09 | 1.3e-05 | 0.797
NCAPH|23397 | 2.3 | 5.592e-09 | 1.7e-05 | 0.853
PIF1|80119 | 2.6 | 7.823e-09 | 1.8e-05 | 0.846
ENPP4|22875 | 0.63 | 8.33e-09 | 1.8e-05 | 0.207
KIF11|3832 | 2.9 | 1.026e-08 | 1.9e-05 | 0.839
ZWINT|11130 | 3.4 | 1.051e-08 | 1.9e-05 | 0.853

Clinical variable #2: 'YEARS_TO_BIRTH'

One gene related to 'YEARS_TO_BIRTH'.

Table S3. Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH | Mean (SD) | 46.7 (16)
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one gene differentially expressed by 'YEARS_TO_BIRTH'

Table S4. Get Full Table List of one gene significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
FAM40B|57464 | 0.4662 | 1.494e-05 | 0.265

Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

30 genes related to 'NEOPLASM_DISEASESTAGE'.

Table S5. Basic characteristics of clinical feature: 'NEOPLASM_DISEASESTAGE'

NEOPLASM_DISEASESTAGE | Labels | N
| STAGE I | 9
| STAGE II | 37
| STAGE III | 16
| STAGE IV | 15
| Significant markers | N = 30

List of top 10 genes differentially expressed by 'NEOPLASM_DISEASESTAGE'

Table S6. Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM_DISEASESTAGE'

| kruskal_wallis_P | Q
--- | --- | ---
DIAPH3|81624 | 4.974e-06 | 0.0231
YJEFN3|374887 | 7.87e-06 | 0.0231
C14ORF80|283643 | 8.277e-06 | 0.0231
ZWILCH|55055 | 8.88e-06 | 0.0231
C8ORFK29|340393 | 9.467e-06 | 0.0231
CENPJ|55835 | 9.755e-06 | 0.0231
TRIP13|9319 | 1.028e-05 | 0.0231
DCAF15|90379 | 1.044e-05 | 0.0231
MCM10|55388 | 1.566e-05 | 0.0309
CCT6P1|643253 | 1.781e-05 | 0.0316

Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE | Mean (SD) | 2.45 (0.98)
|  | N
| T1 | 9
| T2 | 42
| T3 | 8
| T4 | 18
| Significant markers | N = 30
| pos. correlated | 30
| neg. correlated | 0

List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8. Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
SETD8|387893 | 0.6277 | 1.001e-09 | 1.78e-05
FANCI|55215 | 0.553 | 1.838e-07 | 0.00096
LMNB2|84823 | 0.5518 | 1.975e-07 | 0.00096
MCM10|55388 | 0.5503 | 2.166e-07 | 0.00096
DIAPH3|81624 | 0.5486 | 2.88e-07 | 0.00102
TRIP13|9319 | 0.5404 | 3.906e-07 | 0.00112
C8ORFK29|340393 | 0.5509 | 4.413e-07 | 0.00112
YJEFN3|374887 | 0.5361 | 6.006e-07 | 0.0012
CDK1|983 | 0.532 | 6.391e-07 | 0.0012
UHRF1|29128 | 0.5302 | 7.06e-07 | 0.0012

Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE | Labels | N
| N0 | 68
| N1 | 9
| Significant markers | N = 0

Clinical variable #6: 'GENDER'

17 genes related to 'GENDER'.

Table S10. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 48
| MALE | 31
| Significant markers | N = 17
| Higher in MALE | 17
| Higher in FEMALE | 0

List of top 10 genes differentially expressed by 'GENDER'

Table S11. Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 13 significant gene(s) located in sex chromosomes is(are) filtered out.

| W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC
--- | --- | --- | --- | ---
CLN8|2055 | 276 | 2.682e-06 | 0.00476 | 0.8145
MAFG|4097 | 296 | 7.022e-06 | 0.0113 | 0.8011
DPYSL2|1808 | 316 | 1.769e-05 | 0.0234 | 0.7876
FAM117A|81558 | 317 | 1.851e-05 | 0.0234 | 0.787
SFRS8|6433 | 1157 | 3.449e-05 | 0.0408 | 0.7776
CYORF15A|246126 | 217 | 4.766e-05 | 0.0497 | 1
RND2|8153 | 343 | 5.792e-05 | 0.0571 | 0.7695
KIAA0100|9703 | 350 | 7.788e-05 | 0.0727 | 0.7648
TEF|7008 | 352 | 8.468e-05 | 0.0751 | 0.7634
ACLY|47 | 362 | 0.000128 | 0.0947 | 0.7567

Clinical variable #7: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S12. Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY | Labels | N
| HISPANIC OR LATINO | 7
| NOT HISPANIC OR LATINO | 30
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = ACC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

- Clinical data file = ACC-TP.merged_data.txt

- Number of patients = 79

- Number of genes = 17733

- Number of clinical features = 7

Selected clinical features

- For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

- Survival time data

- Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

- if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

- if 'vital_status'==0(alive),

- if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

- if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

- if 'vital_status'==NA,excludes this case in survival analysis and report the case.

- cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

- This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[4] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle